Resilience-oriented Optimal Energy Management of an Integrated Energy Hub Using Teaching-learning-based Optimization

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Chinese explanation / 中文解读

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Original abstract

Energy hubs have been proposed as an integrated structure for the simultaneous management of different energy carriers such as electricity and heat.They can play an important role in increasing the flexibility, reliability, and resilience of energy systems.However, due to the nonlinear, multidimensional, and multiple-constrained nature of energy hub management problems, development of efficient optimization models for optimal operation of these systems remains a major challenge.In this study, an optimization framework for energy hub management, with the aim of increasing system resilience and maintaining energy balance, is presented.In the proposed model, the energy hub structure includes a photovoltaic system, a combined heat and power (CHP) unit, an electric battery, a thermal storage tank, electric vehicles, and power exchange with the main grid.A mathematical model to describe the interaction between different components of the system, in which the decision variables include the power generation of the resources, the charging and discharging power of the storage systems, and the charging status of the equipment in a 24-hour time horizon, is developed.Also, operational constraints including the balance of electrical and thermal power, equipment capacity limitations, and energy storage dynamics are included in the model.The meta-heuristic Teaching-Learning-Based Optimization (TLBO) algorithm is used to solve the optimization problem.Results of simulation studies, conducted in two different scenarios including normal operating conditions and a disruption scenario caused by the exit of the solar power plant, show that under normal conditions, the TLBO algorithm can maintain complete balance between the generation and demand of electrical and thermal energy in all hours of the study period.In the disruption scenario, the system is able to fully meet the network demand using alternative resources.Analysis of the results shows that under these conditions, CHP unit production increased by about 4.6%, electric battery use increased by about 289%, and electric vehicle use increased by about 136%, while thermal storage use decreased by about 15.5%.These results indicate that the proposed framework can play an effective role in increasing the flexibility and resilience of future energy networks.

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